The present invention relates to integrated optics, and more specifically, to integrated holographic information processing.
Training of Deep Neural Nets (DNNs) is a computationally demanding task. A large fraction of the computational workload is spent on evaluating signal propagation through the synaptic network that connects the consecutive levels of neurons in the neural net. This synaptic network connects each neuron in a next layer of M neurons to all N neurons in the previous layer, where the strength of each “synaptic” connection is set by a weighting factor. The total input signal to a next-level neuron is given by the weighted sum of output signals from all active neurons in the previous level. Depending on whether this sum reaches a certain threshold level, the neuron will fire or not.
The computational cost of the synaptic processing step for all neurons comprises M×N memory accesses to the weight values, M×N multiplications and M×N additions.
To train the neural network for performing a specific task, all synaptic weight values have to be optimized. This is performed in a training algorithm in which the response of the neural network to an input signal is compared with the desired output. Then the synaptic weights are updated in an iterative procedure to minimize the difference between the output and the desired output. The well-known Back Propagation training process is an established method to determine the synaptic weight factors. One iteration of the backpropagation algorithm requires 3×M×N memory reads, 3×M×N multiplications, 3×M×N additions and M×N memory stores.
It has been shown that speed and power consumption of the weight optimization processing steps (evaluation and update) can be improved massively by performing these steps using analog computation on a dedicated hardware array of M×N nodes where each node locally stores and processes its corresponding weight value. This method overcomes the memory access bottleneck and allows a parallel optimization of all synaptic weights in one connection layer.
Synaptic weight processing using holographic weight storage in photorefractive materials has been demonstrated in free-space optic setups. These setups typically use high-power lasers as light sources, liquid crystal light valves as optical modulators, CCD cameras as detectors and mm to cm scale photorefractive crystals as storage media. However, these systems are hampered by the large size and low stability of the bulk optics and the low speed of the optical modulators and detectors.
In “Holographic interconnections in photorefractive waveguides”, Applied Optics, vol. 30, pp. 2324-2333, of 1991, the authors David D. Brady and Demetri Psaltis proposed a partially integrated vector matrix multiplier. In this proposal, the matrix coefficients are stored in a photorefractive planar waveguide and the light beams for matrix evaluation are applied and extracted using integrated optics input and output waveguides. The holographic gratings are written using out-of-plane beams, free space optics and a spatial light modulator (liquid crystal light valve) to encode the information.